Contact and Force Detection using Hybrid Estimation

1
Contact and Force Detection using Hybrid
Estimation

• Lars Blackmore
• Brett Kennedy and Eric Baumgartner

2
Overview

• Part A Introduction and Approach
• Contact and Force Detection Problem Statement
• Existing approaches
• Different approach Hybrid Estimation
• Modeling LEMUR II-B manipulator
• Part B Experimental Results
• Contact, Force Detection with Shoulder Torque
  Sensor
• Contact, Force Detection with no Force/Torque
  Sensing
• Conclusion

3
Problem Statement

• With limited sensing capabilities
• Detect contact of manipulator endpoint with
  environment
• Estimate force at endpoint
• What is the minimum set of sensors required?

LEMUR II-B
LEMUR II-A
Images courtesy of Caltech/JPL
4
Existing Approach

• Solution used for MER instrument deployment
  device
• Hardware contact switches detect contact
• Additional mass, volume
• Must be mechanically robust to unexpected
  contact, dust, debris
• Force estimation uses
• Knowledge of contact from switch
• Accurate compliance model,
• to determine overdriving state, and hence tip
  force
• What if contact switch not available?

Contact Sensor
Rock Abrasion Tool
Image courtesy of Caltech/JPL
5
Alternative Approach

• Alternative Approach
• Instead of using dedicated sensor
• Use all available information to infer hidden
  state based on a model of system
• Contact detection
• do observations fit model of contact, or of free
  motion?

6
Hybrid Estimation

• Would like to estimate hidden state
• Kalman filters typically used for state
  estimation with continuous system dynamics
• In manipulation case, hidden state is a hybrid of
  both discrete and continuous components
• Contact state ? contact, no-contact
• Tip force
• Link bend angle
• Estimation with hybrid systems ? Hybrid Estimation

7
Classical Estimation

• Purely continuous case use Kalman Filters to
  estimate state from noisy observations
• Kalman filtering is a probabilistic approach
• Handles noisy sensors
• Represents model uncertainty explicitly
• Robust to anomalous observations

Continuous system model
Noisy observations
Estimated most likely state
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Classical Estimation

• Kalman Filtering
• Calculate belief state about hidden variables
• Approximate as Gaussian
• Predict/update cycle
• Start with belief state at t-1
• Predict belief state at t using system model
• Use measurement at t to adjust the belief state

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Hybrid Estimation

• Hybrid case probabilistic hybrid model of system
• Stochastic transitions between discrete modes
• Different continuous dynamics for each mode

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Hybrid Estimation

• Hybrid case estimate hybrid state from noisy
  observations

Continuous state
Discrete mode
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Hybrid Estimation

• Examples of estimates that can be obtained
• Most likely mode (is there contact?)
• Probability of being in given mode, e.g. contact
• Mean, covariance of hidden state, e.g. tip force
• Conditional distribution of hidden state, e.g.
  tip force given that we have contact
• Probabilistic inference, similar to Kalman Filter
• Any-time, any-space algorithm
• Relies on hybrid system model

12
Overview

• Part A Introduction and Approach
• Contact and Force Detection Problem Statement
• Existing approaches
• Different approach Hybrid Estimation
• Modeling LEMUR II-B manipulator
• Part B Experimental Results
• Contact, Force Detection with Shoulder Torque
  Sensor
• Contact, Force Detection with no Force/Torque
  Sensing
• Conclusion

13
Model Learning

• How do you obtain hybrid system models?
• Model learning approach
• Combine engineering knowledge and on-line
  learning
• Automatic hybrid model learning is an opportunity
  for future research
• For this work, employed an intermediate approach
• Discrete modes identified manually
• Linear least squares parameter estimation used to
  learn continuous dynamics within each mode

14
LII-B Manipulator Model

• Need to model
• Compliance of manipulator links
• Motor torque response to voltage commands

15
Compliance Model

• Assume linear elastic response for small
  deflections
• During contact, assume no slip at endpoint

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Compliance Model

• Now learn compliance parameters using
  experimental data
• Contact experiments carried out using LEMUR II-B

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Compliance Model

• Iterative linear least squares parameter
  estimation
• Good model fit

18
Motor Model

• Very complex behavior to model using traditional
  methods
• example contact

Hysteresis in relationship
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Motor Model

• Very complex behavior to model using traditional
  methods
• But can identify different operational modes
• Free
• Driving
• Holding
• Backdriven
• behavior within each mode can be modeled

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Motor Model

• Free
• After stiction transient, joint velocity
  approximately proportional to commanded voltage

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Motor Model

• Driving
• motor does work against manipulator stiffness
• bend angle ?q increases
• monotonic relationship between V and torque

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Motor Model

• Holding
• Motor can react large torques with small V
• ?q is constant
• V gives no information about torque

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Motor Model

• Backdriven
• Voltage is small or zero
• Motor is driven backwards under applied load
• ?q reduces towards zero

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Motor Model

• Discrete modes
• Free, driving, holding, backdriven
• Behavior within each mode learnt using parameter
  estimation
• Learnt parameters still have significant
  uncertainty
• Some effects still unmodeled
• Will the model be accurate enough for estimation?
• Hybrid discrete/continuous model useful tool for
  modeling complex system behavior

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Motor Model Discrete Transitions

• Now we have discrete modes and dynamics
• Need to specify transitions between modes
• Transition model gives estimator more information
• Biases mode estimates

NB Not all transitions shown, for clarity
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Overview

• Part A Introduction and Approach
• Contact and Force Detection Problem Statement
• Existing approaches
• Different approach Hybrid Estimation
• Modeling LEMUR II-B manipulator
• Part B Experimental Results
• Contact, Force Detection with Shoulder Torque
  Sensor
• Contact, Force Detection with no Force/Torque
  Sensing
• Conclusion

27
Force Estimation with LII-B

• LEMUR II-B has accurate torque sensor at shoulder
• Detect contact and estimate tip forces using
• Shoulder torque sensor
• Encoder data
• Motor control voltages

Image courtesy of Caltech/JPL
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Estimation with Torque Sensor

• What does LII-B shoulder torque sensor tell us
  about tip forces?

F
T
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Estimation with Torque Sensor

• How well can Hybrid Estimation estimate tip
  forces using
• Compliance model
• Motor model
• Shoulder torque sensor?
• Torque sensor gives accurate information about
  perpendicular component
• Compliance and motor model fills in the gaps

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Estimation with Torque Sensor

• Contact scenarios with different moment arms
• As moment arm decreases, torque sensor yields
  less and less information
• Estimation relies more heavily on model
• 10 mode sequences tracked

T
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Estimation with Torque Sensor

• Results moment arm at 0.15m
• Average error 7

Estimate smoother than measured force
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Estimation with Torque Sensor

• Force estimate accurate except for very small
  moment arm

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Estimation with Torque Sensor

• Conclusion
• Hybrid Estimation able to fill in missing
  information using compliance and motor model
• Force estimates accurate to within 10 except for
  very small moment arm
• Model-based approach means changing sensor type
  or location is simple

34
Estimation without Torque Sensor

• Detect contact, forces at LEMUR II-B endpoint
• without any force/torque sensing

Image courtesy of Caltech/JPL
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Ad-hoc Contact Detection

• How would you make a contact detector without
  force sensing?
• Doesnt achieve desired velocity if have contact?

Free motion
Contact (tip stationary)
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Ad-hoc Contact Detection

• Need to look at lower level system dynamics
• How does commanded voltage relate to observed
  encoder motion in different contact states?
• Main point information is there how do we
  detect contact?

Free motion
Contact
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Ad-hoc Contact Detection

• How would you build a detection scheme now?
• Threshold the voltage?
• What about commanding different velocities?
• What about transients? (noise, stiction)

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Contact Detection with Hybrid Estimation

• Models of system behavior for each possible mode
  (contact, no contact)
• Estimator looks at observations and determines
  evidence for each of models being true

Initially both free and contact look likely
Evidence against free builds up as V continues to
increase
39
No Torque Sensor Results

• Detection not possible without torque sensor
  unless computational resource allocation
  increased
• Increased allocation to 50 tracked sequences
• Typical results

40
No Torque Sensor Results

• Contact detected in all cases for force gt 4N
• Becomes unreliable below this threshold
• Average detection delay 0.37s
• Average duration error 21
• Consistently estimates shorter duration, perhaps
  backdriving model could be improved
• Reliable contact detection is possible using only
  motor voltages and encoder counts
• Are the computational resources available?

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No Torque Sensor Results

• Tip force estimates
• Typical result
• On average, force estimate accurate to 28

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No Torque Sensor Summary

• Probabilistic approach gives reliable contact
  detection using only motor voltages and encoder
  data
• Evidence for contact builds up over several time
  steps
• Robust to noise in sensors and modeling error
• Relatively accurate tip force estimation also
  possible
• Detailed validation not yet carried out
• Significantly greater computational resources
  required than for detection with torque sensor

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Experimental Lessons Learnt

• Performance is highly sensitive to endpoint slip
• Motion caused by slip attributed to increase in
  ?q, forces greatly overestimated
• Performance depends on control law used
• Problems occur when using joint space controller
• Best performance when using cartesian trajectory
  control
• Performance is sensitive to noise parameters in
  model
• Difficult to model using engineering knowledge
• Learning approach likely to be very useful

44
Computational Issues

• Estimator not implemented on-line due to time
  restrictions
• Off-line implementation not optimized for speed,
  memory
• Algorithm is any-time, any-space
• Tradeoff between sensor capabilities and
  computational resources

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Future Research Opportunities

• Further testing and validation of this approach
• What sensors are necessary to achieve
  requirements?
• Automated learning of hybrid models
• Active estimation
• Gain more information by actively probing a
  system
• Design safe control inputs that distinguish
  optimally between uncertain modes
• (Manipulator path planning with obstacles)

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Conclusion

• Using very limited sensor information, Hybrid
  Estimation can detect contact and estimate tip
  forces by reasoning about hybrid system models

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Questions?